Computer-aided detection/diagnosis (CAD) systems have shown significant potential towards reading image volumes more efficiently. A common theme and basis of CAD methods is image segmentation and classification. Many established methods built on image intensity based and/or shape based parameters, have been used to perform such analyses. The classification problem is typically solved using machine-learning methods, which can be either supervised or unsupervised.
While a goal of breast imaging CAD systems is to detect and classify pathological findings, an important initial step is to classify normal breast tissue types, which can potentially serve to improve the specificity of tumor detection.